Data-intensive computing
Subtitle: architectures, algorithms, and applications
By: [edited by] Ian Gorton, Deborah K. Gracio
Publisher: Cambridge University Press
Published: 2013
Place of publication: Cambridge
Language: eng
ISBN-13: 9780521191951
Subjects: Computer storage devices; COMPUTERS / Database Management / General; Database management; Software architecture; High performance computing; Data transmission systems
Description: "A reference describing the general principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today"--

"A Challenge for the 21st Century Introduction In a world of rapid technological change such as the one we inhabit, it's occasionally instructive to contemplate how much things have changed in the last few years. For many, remembering life without the ability to view the World Wide Web (WWW) through the windows of a browser will be difficult (if not impossible for less 'mature' readers). And is it only seven years since YouTube, a Web site that is ingrained in so many facets of modern life, first came to life? How did we all really survive without FaceBook all those (actually, about 5) years ago? Various estimates put the amount of data stored by consumers and businesses around the world in 2010 in the vicinity of 13 exabytes, with a growth rate of 20--25% per annum. That's a lot of data. No wonder IBM is pursuing building a 120 petabyte storage array . There's obviously going to be a market for such devices in the future. As data volumes of all types, from video and photos to text documents and binary files for science, continue to grow in number and resolution, it's clear we have genuinely entered the realm of data intensive, or big data , computing."--
Notes: Includes bibliographical references and index.
Table of contents: title: Machine generated contents note: 1. Data-intensive computing: a challenge for the 21st century / Ian Gorton and Deborah K. Gracio; 2. The anatomy of data-intensive computing applications / Ian Gorton and Deborah K. Gracio; 3. Hardware architectures for data-intensive computing problems: a case study for string matching / Antonino Tumeo, Oreste Villa and Daniel Chavarría-Miranda; 4. Data management architectures / Terence Critchlow, Ghaleb Abdulla, Jacek Becla, Kerstin Kleese-Van Dam, Sam Lang and Deborah L. McGuinness; 5. Large scale data management techniques in cloud computing platforms / Sherif Sakr and Anna Liu; 6. Dimension reduction for streaming data / Chandrika Kamath; 7. Binary classification with support vector machines / Patrick Nichols, Bobbie-Jo Webb-Robertson and Christopher Oehmen; 8. Beyond MapReduce: new requirements for scalable data processing / Bill Howe; 9. Letting the data do the talking: hypothesis discovery from large-scale datasets in real time / Christopher Oehmen, Scott Dowson, Wes Hatley, Justin Almquist, Bobbie-Jo Webb-Robertson, Jason McDermott, Ian Gorton and Lee Ann McCue; 10. Data-intensive visual analysis for cybersecurity / William A. Pike, Daniel M. Best, Douglas V. Love and Shawn J. Bohn.